# Qdrant Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/qdrant
> Markdown URL: https://aitinkerers.org/technologies/qdrant.md
> Technology record last updated: 2026-09-18T14:19:44Z
> Generated: 2026-09-20T19:46:41Z

Qdrant is an open-source, Rust-powered vector database and search engine: it delivers high-performance, scalable similarity search for AI applications.

Qdrant functions as a production-ready vector database, purpose-built in Rust for unmatched speed and reliability, even when processing billions of high-dimensional vectors. It provides a convenient API to store, search, and manage vector embeddings (points) along with optional metadata (payloads). Key features include advanced filtering on those payloads, support for multiple distance metrics (Cosine, Dot Product, Euclidean), and cloud-native scalability. Developers leverage Qdrant for critical AI workloads like Retrieval-Augmented Generation (RAG) systems and large-scale recommendation engines, deploying via Docker, self-hosting, or the managed Qdrant Cloud service.

- Official technology site: https://qdrant.tech
- Public AI Tinkerers demos and talks: 17
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Don’t Just Build an App: Build a Playground - Designing flexible, high-growth ecosystems that scale seamlessly.](https://orange-county.aitinkerers.org/talks/rsvp_FR1uOvCMPCI)

Oddjob lets you spin up teams of AI agents that plan and execute multi-step work as missions, cycles, and pipelines — extracting knowledge, running research, generating documents and datasets, and completing tasks end to end. It can build and deploy working software and mini-apps on demand through App Forge, stand up org and team structures through Org Forge, and turn a document template into a live form agent that captures a structured schema automatically. It grounds every agent in your own data with retrieval-augmented generation, a knowledge graph, and reusable templates, and routes work through configurable LLM providers with automatic failover to a local model when a provider is unavailable.

- Event context: AI Tinkerers Orange County: Tuesday, July 21, 2026 at Centercode — 2026-07-22 — Orange County
- Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_FR1uOvCMPCI

### [The Prompt That Worked in Isolation and Died in Production: Eval-Driven Prompt Engineering for a Real-Time Interview Copilot](https://seoul.aitinkerers.org/talks/rsvp_S4LRW_RGb3U)

InterviewMate (interviewmate.tech) is a real-time interview copilot I built and run in production: it listens to a live interview, transcribes it as it happens, and streams back answer suggestions grounded in the candidate's own experience — in under a second. The pipeline is Deepgram streaming STT into a FastAPI WebSocket Q&A pipeline, RAG retrieval over my own past answers, and a Claude streaming response. I'll run the working system live. Then I'll show the part that actually mattered: the eval harness behind the system prompt. It started as a bug — my copilot answered the viral "car wash" question correctly ("drive") while the same model, called with no prompt around it, said "walk," and I couldn't explain which prompt layer was responsible. So I turned the production prompt into a controlled experiment: 6 prompt conditions, 20 runs each, showing STAR-style structured reasoning beating context injection by 2.83x — and a follow-up where that same winning prompt collapsed to 0–30% once I dropped it into the real 60-line production prompt. Code, eval logs, and the pipeline architecture will all be on screen.

- Event context: AI Tinkerers Seoul: Inaugoral Demo Night — 2026-07-02 — Seoul
- Public talk page: https://seoul.aitinkerers.org/talks/rsvp_S4LRW_RGb3U

### [911automate](https://montreal.aitinkerers.org/talks/rsvp_Fxb5kGzWsq4)

A RAG system POC for emergency protocols

- Event context: AI Tinkerers Montreal - April Demo Night — 2026-04-22 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_Fxb5kGzWsq4

### [AI-Agents that learn from humans-in-the-loop](https://nurnberg.aitinkerers.org/talks/rsvp_YaF-2Es_QW0)

Enterprise AI Platform that not only integrates existing data sources and operational systems, but that enables AI Agents who can proactively identify the right employees to acquire and preserve missing knowledge or hand-off and delegate tasks.

- Event context: AI Tinkerers Nürnberg: April Meetup — 2026-04-22 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_YaF-2Es_QW0

### [LogAnalyzer: Cuando el llm no solo analiza sino que explica](https://manizales.aitinkerers.org/talks/rsvp__qJVwdb18Ls)

Es una herramienta open source que detecta anomalías en logs y además enseña a interpretarlas usando un LLM. No solo alerta, sino que actúa como un “mentor” para analistas.

- Event context: 🚀 ¡14vo Encuentro de AI Tinkerers Manizales! 🤖 — 2026-03-25 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp__qJVwdb18Ls

### [VLLM and Qdrant - GPU goes Brrrr!](https://manchester-nh.aitinkerers.org/talks/rsvp_RGPw96tcjiA)

This demo goes over the fundamentals of VLLM and the QDrant vector database. We'll spin up some Docker containers with the LLM, Database and Embedding model, and then run some interesting benchmarks. I'll demonstrate just how much more powerful VLLM can be on hardware when compared to sequential model runners.

- Event context: AI Tinkerers Manchester (Bedford), NH - March 2026 Meetup — 2026-03-18 — Manchester NH
- Public talk page: https://manchester-nh.aitinkerers.org/talks/rsvp_RGPw96tcjiA

### [Practical LLM Integration: Lessons from Building a SEC Filing Analysis Project](https://houston.aitinkerers.org/talks/rsvp_Vt9RZstb_VY)

A live coding demo walking through an LLM-powered document analysis system. I'll run the pipeline live and show the code that makes it work. Using regex and rule-based filters to reduce LLM API costs by 80%+ Extracting structured data from unstructured documents with Claude Implementing semantic caching with Qdrant to avoid redundant API calls Tracking LLM costs and latency with Langfuse

- Event context: AI Tinkerers Houston: Working Code, Live Demos, and Architectures for 2026 — 2026-01-13 — Houston
- Public talk page: https://houston.aitinkerers.org/talks/rsvp_Vt9RZstb_VY

### [Tally: Building the Ambient AI interfacer With long- term Memory](https://london.aitinkerers.org/talks/rsvp_wEQ0cuZsepE)

Tally is a new wearable AI device that captures continuous real-world context through dual 180° cameras and on-device audio — enabling an always-present assistant that understands your environment, remembers your day, and can take actions on your behalf. In this talk, I’ll walk through how we designed Tally’s hardware and software stack from the ground up: Ear-level camera earbuds with 360° capture Real-time video ingestion via RTMP/LiveKit Continuous memory creation: transcripts, embeddings, moments, and timelines Our “Speech-to-App” system that turns natural language into actions Building a developer SDK so anyone can build ambient agents powered by user context Key challenges in reliability, privacy, and low-bandwidth streaming This session will include architecture diagrams, live demos, and insights from building Tally through multiple hackathons and investor showcases.

- Event context: AI Tinkerers London Meetup - 25th November 2025 — 2025-11-25 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_wEQ0cuZsepE

### [Building Pedagogically-Aligned AI Tutors: A Multi-Agent Framework for Critical Thinking Development](https://dhaka.aitinkerers.org/talks/rsvp_350rq1EzRG8)

This talk presents a novel multi-agent intelligent tutoring system that prioritizes student reasoning over answer-giving. Unlike typical LLM-powered tutors that default to information delivery, our framework implements a supervised architecture where specialized agents work in concert: a Socratic agent conducts grade-appropriate dialogues, a supervisor ensures pedagogical alignment and prevents answer leakage, an evaluation layer tracks reasoning quality through milestone progression, and a RAG component provides curriculum-grounded hints while maintaining strict knowledge boundaries. The system enforces a misconception-based learning workflow where students must diagnose, explain, and correct errors using evidence—with the AI learning only from student input. An 8-week classroom study across grades 5-10 (627 students, 10 teachers) demonstrated statistically significant learning gains (p=.03), with teachers reporting substantial improvements in evidence-based reasoning and productive struggle. This session will cover the technical architecture, the challenge of separating student-taught knowledge from RAG-retrieved context, the orchestration loop that regenerates responses based on supervisory feedback, and practical insights from real classroom deployment. Attendees will learn how to build AI systems that elevate—rather than bypass—student thinking.

- Event context: AI Tinkerers Dhaka Inaugural Meetup - Dhaka Builds with AI — 2025-11-01 — Dhaka
- Public talk page: https://dhaka.aitinkerers.org/talks/rsvp_350rq1EzRG8

### [Teaching AI to read ancient Maya Glyphs](https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I)

The ancient Maya developed one of the most beautiful, complex, and intriguing writing systems in human history. For centuries, these glyphs recorded stories, rituals, dynasties, and the deep relationship between people, time, and the cosmos. Yet this knowledge was silenced — burned, buried, and forgotten for nearly a thousand years. Even today, only a few thousand people in the world can read Maya writing. I will the development of an AI agent designed to help bridge that gap. Using computer vision and deep learning, the system can detect glyph blocks, identify individual signs, and suggest possible readings by comparing them to a vast epigraphic corpus. It is not perfect — but it is a powerful first step toward using artificial intelligence to preserve and reactivate knowledge that was nearly lost forever. In doing so, we demonstrate how AI can be more than just code — it can be a force for memory, preservation, and cultural resilience. I will be presenting a live demo.

- Event context: AI Tinkerers Montreal – Spooky October 2025 Meetup — 2025-10-21 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_Z7tI1x8ww7I

### [No Data for the Witch’s Cauldron: Using Safe, Open-Source AI Chats systems](https://montreal.aitinkerers.org/talks/rsvp_bNbMDKHWgAc)

Stop building chat applications, reuse existing ones and extend them ! Sharing a technical journey to use a safe and compliant solution for chatting with private project data, leveraging local and remote LLMs, internal RAG systems, MCP servers, live voice, image generation, web search, and a code interpreter. The result is similar to ChatGPT but built entirely on open-source technologies and trusted cloud providers LLM APIs. Using OpenWeb UI as the chat front-end, connected to a foundation model on Azure AI and integrated with internal RAG system for documentation. LiteLLM serves as the OpenAPI layer for both OpenWeb UI and server calls, supporting claude code, codex-cli, and a custom workaround for gemini-cli, as we operate exclusively on AWS Bedrock and Azure AI.

- Event context: AI Tinkerers Montreal – Spooky October 2025 Meetup — 2025-10-21 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_bNbMDKHWgAc

### [EVA - Enhanced Video Archive](https://raleigh.aitinkerers.org/talks/rsvp_9ld1Kd98tv4)

EVA (Enhanced Video Archive) turns sprawling legacy video collections into a searchable knowledge base by indexing transcripts, descriptions, and keyframes, then using RAG to answer text or screenshot queries. Ask for a topic and EVA returns a report with linked screenshots and exact timestamps—so trainers and learners can jump straight to the moments that matter instead of scrubbing through hours of footage.

- Event context: AI Tinkerers - Raleigh Inaugural Meetup (September 2025) — 2025-09-30 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_9ld1Kd98tv4

### [Beyond Text: Building a fast Visual Search Engine](https://berlin.aitinkerers.org/talks/rsvp_7LBNQm7JTWM)

This demo will dive deep into the architecture and implementation of Youzu Lens, our flagship zero-shot visual search engine. We will explore how we leverage vector models to generate rich, 1280-dimensional image embeddings, enabling precise product recognition and similarity matching. The session will cover the pipeline, from multi-tenant product ingestion and GPU-accelerated inference (PyTorch/CUDA) to high-performance vector similarity search engine that achieves sub second query times. We will demonstrate architectural diagrams, showcasing how we handle millions of products with strict data isolation and achieve superior accuracy compared to standard implementations. We can show you how it works in one of customers - https://ro.vivre.eu/

- Event context: AI Tinkerers Meetup August 13 — 2025-08-13 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_7LBNQm7JTWM

### [A Simple production Ready RAG pipeline](https://dublin.aitinkerers.org/talks/rsvp_ILfCX6RtjnQ)

A production ready implementation of RAG showing step by step how RAG with qdrant can be done and coming out of it knowing the fundamental basics of RAG

- Event context: AI Tinkerers - Dublin Event (June) — 2025-06-26 — Dublin
- Public talk page: https://dublin.aitinkerers.org/talks/rsvp_ILfCX6RtjnQ

### [Deconstructing RAG: Building, Iterating, and Exploring Advanced Patterns](https://milan.aitinkerers.org/talks/rsvp_pNg9pDV0OHk)

This talk dives into the practical construction of Retrieval Augmented Generation (RAG) systems, starting from a foundational "barebones" pipeline. We'll walk through each core component: query rewriting for clarity, efficient vector storage and retrieval with Qdrant, the crucial role of reranking for relevance, and finally, the generation step. Beyond this core, I'll share insights and lessons learned from experimenting with more advanced RAG variations, including Agentic RAG for complex tasks, Hierarchical RAG for handling large document sets, image-based RAG for multimodal understanding, and Graph RAG for leveraging relationships in data. Attendees will gain a clear understanding of how to build their own RAG, make informed design choices, and explore pathways for enhancing its capabilities.

- Event context: AI Tinkerers Milan - May 8, 2025 — 2025-05-08 — Milan
- Public talk page: https://milan.aitinkerers.org/talks/rsvp_pNg9pDV0OHk

### [epilepsy ally: a chatbot to help parents of kids with epilepsy](https://chicago.aitinkerers.org/talks/rsvp__O0u3ll2BrE)

Some kids have seizures that cannot be stopped with medicine - for many, brain surgery is the only option. As a parent of a child with intractable epilepsy, I have found it daunting to figure out what my child needs. I built a chatbot that sifts through medical jargon and the latest research and helps parents who are facing or who's child has already had surgery.

- Event context: AI Tinkerers Chicago April Meetup — 2025-04-15 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp__O0u3ll2BrE

### [Breathing life into dead documentation](https://nashville.aitinkerers.org/talks/rsvp_l2LhwBb-CfY)

Using Generative AI to allow dev teams and enterprises to automate documentation based on changes deployed into their environment. Jira and Confluence are very common tools used within tech shops. Jira to track issues and tickets. Confluence to document architectures, application stacks, API's etc. I have a love/hate relationship with documentation so I decided to automate it. For quick POC I built a series of workflow automations using n8n and n8n AI agents to identify when changes are made in an environment and then update confluence documents using LLM. This required various document retrieval and tracking techniques, various agents and such. I am beginning the process of transferring this over to Python and PydanticAI but that is early. So I would be presenting this in N8N running on my laptop.

- Event context: AI Tinkerers Nashville – April 9th, 2025 — 2025-04-09 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_l2LhwBb-CfY

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